Abstract: A method, device and a computer program product for determining a training load distribution of a user is provided. Heart rate is continuously measured by a heart rate sensor. An aerobic training effect and load are calculated using intensity values and divided into categories of Aerobic Low and Aerobic High. An anaerobic training effect and load are calculated using determined characteristics related to high intensity periods of an exercise.

Abstract: A method and a system for determining the maximum heart rate, called HRmax of a user of in a freely performed physical exercise and using an apparatus with software and memory. An intensity model takes account of, in addition to heart rate, respiration rate and/or kinetics information.

Abstract: A method, device and a computer program product for determining a training load distribution of a user is provided. Heart rate is continuously measured by a heart rate sensor. An aerobic training effect and load are calculated using intensity values and divided into categories of Aerobic Low and Aerobic High. An anaerobic training effect and load are calculated using determined characteristics related to high intensity periods of an exercise.

Abstract: An apparatus and method for determining injury risk from plurality of exercises using a device with a heart rate sensor, a processor, memory including a resident memory, an output device and software. For example the method may: determine and store values of Training load of each exercise in the resident memory obtaining a register, perform a HRV-based Recovery Test and storing Recovery values in the register, calculate indices depicting Short Term Training Load, ratio of Short Term Training Load to Long Term Training Load, and Recovery, calculate weighting factors for each said index, correct each index with corresponding weighting factor to obtain weighted indices, calculate a value of the Injury Risk using the weighted indices, and display the value of Injury Risk.

Abstract: A signaling device for increasing visibility of a mobile robot. The signaling device comprises (a) a base section, attached with a rotational joint to a side of the mobile robot, said rotational joint allowing the device to rotate clockwise and/or anticlockwise relative to the mobile robot, the rotational joint constructed and adapted to return the device to an equilibrium position; and (b) a body section comprising a mast, the body section flexibly attached to the base section at a lower extreme of the body section.

Abstract: A method and system for determining training status of a user from exercises using a device with a heart rate sensor, a processor, memory, an output device and software. The training status is selected from a fixed group of alternatives. Each exercise is monitored using the heart rate sensor. Chosen exercise characteristics of each executed exercise are determined using obtained heart rate data and the determined characteristics of each executed exercise are stored in a memory. The chosen exercise characteristics include values of at least following variables: a date of the exercise, a value depicting physical readiness level for exercise during the exercise, a value depicting a training load of the exercise. When the exercises have been executed, values of selection variables are calculated using the stored exercise characteristics in the memory.

Abstract: A signaling device for increasing visibility of a mobile robot. The signaling device comprises a signaling section, a body section, and a base section. The invention also discloses a system for increasing visibility of a mobile robot. The system comprises a mobile robot, a signaling device attached to the mobile robot and a remote terminal configured to at least one of monitoring and controlling at least one of the mobile robot and the signaling device.

Abstract: Providing a next workout recommendation (NWR) comprises calculating an activity class for a user; determining a total training load target based on the activity class and a training goal; determining labels for performed workouts, wherein a label describes a training effect of a workout and wherein the labels are further divided into intensity categories of aerobic low load, aerobic high load and anaerobic load; detecting a training load distribution (TLD), wherein the TLD comprises a cumulative training load sum for the intensity categories; determining the intensity category based on recovery time from the previous training, a weekly training load compared to a weekly training load target and the TLD, to select a label for the NWR; and determining the NWR based on the labels of the previous workouts, determined weekly training load and the TLD, wherein the NWR comprises at least an aerobic/anaerobic training effect target and/or the selected label.

Abstract: A method and apparatus for improving recognition of anaerobic sections of exercise and to providing feedback on training load or training effect reflecting energy systems used and trained during exercise. The method determines an ‘oxygen debt’ like cumulative physiological sum (usually training effect TE as EPOC value) brought on by a change in a body homeostasis and its aerobic and anaerobic values. Particularly anaerobic value may be determined by a procedure, where a total EPOC (or TRIMP) is determined as a total sum and an aerobic part calculated in a known manner, is deducted from the total sum.

Abstract: The application presents providing next workout recommendation(s) for a user. The current training load for the next workout recommendation is determined based on a present activity class, a phase of the micro-cycle of successive training days and the sum of the training load values for a number of past trainings. The present activity class of the user corresponds to a present fitness level of the user. A total target training load is determined based on the present activity class and the training goal, which relates to maintaining, increasing or increasing fast the fitness level of the user. The phase of the micro-cycle of successive training days for the next workout recommendation is determined by the realized training load value(s) for daily training session(s) of at least one previous day from training history data, as a percentage of the sum of the training load values for a number of past trainings.

Abstract: A method and a system for determining the maximum heart rate, called HRmax of a user of in a freely performed physical exercise and using an apparatus with software and memory. An intensity model takes account of, in addition to heart rate, respiration rate and/or kinetics information.

Abstract: A method and a system for determining the maximum heart rate of a user in a freely performed physical exercise and using an apparatus with software and memory. An intensity model takes account of, in addition to heart rate, respiration rate and/or kinetics-information. The kinetics information depicts change in excess post-exercise oxygen consumption (EPOC), more generally the direction of cumulative physiological disturbance in homeostasis, whether it is at steady state, on-response (rising) or off-response (descending) to decrease the value of the determined maximal heart rate to obtain a result.

Abstract: Disclosed is a storage device configured to store, maintain and shelter a mobile robot. The storage device comprises a housing, a port, a battery station, a processing component and a communication component. Also disclosed is a system configured to store, maintain and shelter a mobile robot. The system comprises the storage device, a mobile robot and a server. Also disclosed is a method for at least one of storing and maintaining a mobile robot.

Abstract: A system of estimating acclimatization to environmental conditions, and providing a corrected fitness level estimates based on said acclimatization level. The environmental conditions are particularly related to heat and altitude. At first previous training history is provided including a plurality of day records, where each day record includes a date stamp and information regarding altitude and optionally temperature and humidity and training history including timestamp and training load data from executed exercises in stored records.

Abstract: A method, device and a computer program product for determining a training load distribution of a user is provided. Heart rate is continuously measured by a heart rate sensor. An aerobic training effect and load are calculated using intensity values and divided into categories of Aerobic Low and Aerobic High. An anaerobic training effect and load are calculated using determined characteristics related to high intensity periods of an exercise.

Abstract: A method and system for determining training status of a user from exercises using a device with a heart rate sensor, a processor, memory, an output device and software. The training status is selected from a fixed group of alternatives. Each exercise is monitored using the heart rate sensor. Chosen exercise characteristics of each executed exercise are determined using obtained heart rate data and the determined characteristics of each executed exercise are stored in a memory. The chosen exercise characteristics include values of at least following variables: a date of the exercise, a value depicting physical readiness level for exercise during the exercise, a value depicting a training load of the exercise. When the exercises have been executed, values of selection variables are calculated using the stored exercise characteristics in the memory.

Abstract: The application presents providing next workout recommendation(s) for a user. The current training load for the next workout recommendation is determined based on a present activity class, a phase of the micro-cycle of successive training days and the sum of the training load values for a number of past trainings. The present activity class of the user corresponds to a present fitness level of the user. A total target training load is determined based on the present activity class and the training goal, which relates to maintaining, increasing or increasing fast the fitness level of the user. The phase of the micro-cycle of successive training days for the next workout recommendation is determined by the realized training load value(s) for daily training session(s) of at least one previous day from training history data, as a percentage of the sum of the training load values for a number of past trainings.

Abstract: A method and system for determining training status of a user from exercises using a device with a heart rate sensor, a processor, memory, an output device and software. The training status is selected from a fixed group of alternatives. Each exercise is monitored using the heart rate sensor. Chosen exercise characteristics of each executed exercise are determined using obtained heart rate data and the determined characteristics of each executed exercise are stored in a memory. The chosen exercise characteristics include values of at least following variables: a date of the exercise, a value depicting physical readiness level for exercise during the exercise, a value depicting a training load of the exercise. When the exercises have been executed, values of selection variables are calculated using the stored exercise characteristics in the memory.

Abstract: A method and a system for determining the maximum heart rate, called HRmax of a user of in a freely performed physical exercise and using an apparatus with software and memory means.